From e27a508c59f0d762698b9f2129d748459a3db5c9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Daniel=20Mesejo-Le=C3=B3n?= Date: Fri, 27 Mar 2020 18:21:15 +0100 Subject: [PATCH] Update supported Pandas to v1.0 --- .gitignore | 1 + docs/requirements-docs.txt | 2 +- docs/source/examples/demo_notebook.ipynb | 238 +++++++++++++---------- eland/dataframe.py | 88 +++++++-- eland/ndframe.py | 40 ++-- eland/operations.py | 23 ++- eland/series.py | 4 +- noxfile.py | 2 +- requirements-dev.txt | 2 +- requirements.txt | 2 +- setup.py | 2 +- 11 files changed, 249 insertions(+), 155 deletions(-) diff --git a/.gitignore b/.gitignore index 0eb12e58..f149ff4f 100644 --- a/.gitignore +++ b/.gitignore @@ -43,6 +43,7 @@ ipython_config.py # Environments .env .venv +.nox env/ venv/ ENV/ diff --git a/docs/requirements-docs.txt b/docs/requirements-docs.txt index e196dfd8..7c05f4db 100644 --- a/docs/requirements-docs.txt +++ b/docs/requirements-docs.txt @@ -1,5 +1,5 @@ elasticsearch>=7.0.5 -pandas==0.25.3 +pandas>=1 matplotlib pytest>=5.2.1 git+https://github.com/pandas-dev/pandas-sphinx-theme.git@master diff --git a/docs/source/examples/demo_notebook.ipynb b/docs/source/examples/demo_notebook.ipynb index 69e94dc7..27c12f48 100644 --- a/docs/source/examples/demo_notebook.ipynb +++ b/docs/source/examples/demo_notebook.ipynb @@ -753,7 +753,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 17, @@ -2704,10 +2704,10 @@ " \n", " \n", " 25%\n", - " 410.008918\n", + " 410.012798\n", " 2470.545974\n", " ...\n", - " 251.938710\n", + " 251.682199\n", " 1.000000\n", " \n", " \n", @@ -2720,11 +2720,11 @@ " \n", " \n", " 75%\n", - " 840.617448\n", - " 9738.206675\n", + " 842.233478\n", + " 9735.660463\n", " ...\n", - " 720.026320\n", - " 4.160448\n", + " 720.572969\n", + " 4.271242\n", " \n", " \n", " max\n", @@ -2745,9 +2745,9 @@ "mean 628.253689 7092.142457 ... 511.127842 2.835975\n", "std 266.386661 4578.263193 ... 334.741135 1.939365\n", "min 100.020531 0.000000 ... 0.000000 0.000000\n", - "25% 410.008918 2470.545974 ... 251.938710 1.000000\n", + "25% 410.012798 2470.545974 ... 251.682199 1.000000\n", "50% 640.362667 7612.072403 ... 503.148975 3.000000\n", - "75% 840.617448 9738.206675 ... 720.026320 4.160448\n", + "75% 842.233478 9735.660463 ... 720.572969 4.271242\n", "max 1199.729004 19881.482422 ... 1902.901978 6.000000\n", "\n", "[8 rows x 7 columns]" @@ -2786,33 +2786,35 @@ "\n", "Index: 13059 entries, 0 to 13058\n", "Data columns (total 27 columns):\n", - "AvgTicketPrice 13059 non-null float64\n", - "Cancelled 13059 non-null bool\n", - "Carrier 13059 non-null object\n", - "Dest 13059 non-null object\n", - "DestAirportID 13059 non-null object\n", - "DestCityName 13059 non-null object\n", - "DestCountry 13059 non-null object\n", - "DestLocation 13059 non-null object\n", - "DestRegion 13059 non-null object\n", - "DestWeather 13059 non-null object\n", - "DistanceKilometers 13059 non-null float64\n", - "DistanceMiles 13059 non-null float64\n", - "FlightDelay 13059 non-null bool\n", - "FlightDelayMin 13059 non-null int64\n", - "FlightDelayType 13059 non-null object\n", - "FlightNum 13059 non-null object\n", - "FlightTimeHour 13059 non-null float64\n", - "FlightTimeMin 13059 non-null float64\n", - "Origin 13059 non-null object\n", - "OriginAirportID 13059 non-null object\n", - "OriginCityName 13059 non-null object\n", - "OriginCountry 13059 non-null object\n", - "OriginLocation 13059 non-null object\n", - "OriginRegion 13059 non-null object\n", - "OriginWeather 13059 non-null object\n", - "dayOfWeek 13059 non-null int64\n", - "timestamp 13059 non-null datetime64[ns]\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 AvgTicketPrice 13059 non-null float64 \n", + " 1 Cancelled 13059 non-null bool \n", + " 2 Carrier 13059 non-null object \n", + " 3 Dest 13059 non-null object \n", + " 4 DestAirportID 13059 non-null object \n", + " 5 DestCityName 13059 non-null object \n", + " 6 DestCountry 13059 non-null object \n", + " 7 DestLocation 13059 non-null object \n", + " 8 DestRegion 13059 non-null object \n", + " 9 DestWeather 13059 non-null object \n", + " 10 DistanceKilometers 13059 non-null float64 \n", + " 11 DistanceMiles 13059 non-null float64 \n", + " 12 FlightDelay 13059 non-null bool \n", + " 13 FlightDelayMin 13059 non-null int64 \n", + " 14 FlightDelayType 13059 non-null object \n", + " 15 FlightNum 13059 non-null object \n", + " 16 FlightTimeHour 13059 non-null float64 \n", + " 17 FlightTimeMin 13059 non-null float64 \n", + " 18 Origin 13059 non-null object \n", + " 19 OriginAirportID 13059 non-null object \n", + " 20 OriginCityName 13059 non-null object \n", + " 21 OriginCountry 13059 non-null object \n", + " 22 OriginLocation 13059 non-null object \n", + " 23 OriginRegion 13059 non-null object \n", + " 24 OriginWeather 13059 non-null object \n", + " 25 dayOfWeek 13059 non-null int64 \n", + " 26 timestamp 13059 non-null datetime64[ns]\n", "dtypes: bool(2), datetime64[ns](1), float64(5), int64(2), object(17)\n", "memory usage: 3.2+ MB\n" ] @@ -2838,35 +2840,37 @@ "\n", "Index: 13059 entries, 0 to 13058\n", "Data columns (total 27 columns):\n", - "AvgTicketPrice 13059 non-null float64\n", - "Cancelled 13059 non-null bool\n", - "Carrier 13059 non-null object\n", - "Dest 13059 non-null object\n", - "DestAirportID 13059 non-null object\n", - "DestCityName 13059 non-null object\n", - "DestCountry 13059 non-null object\n", - "DestLocation 13059 non-null object\n", - "DestRegion 13059 non-null object\n", - "DestWeather 13059 non-null object\n", - "DistanceKilometers 13059 non-null float64\n", - "DistanceMiles 13059 non-null float64\n", - "FlightDelay 13059 non-null bool\n", - "FlightDelayMin 13059 non-null int64\n", - "FlightDelayType 13059 non-null object\n", - "FlightNum 13059 non-null object\n", - "FlightTimeHour 13059 non-null float64\n", - "FlightTimeMin 13059 non-null float64\n", - "Origin 13059 non-null object\n", - "OriginAirportID 13059 non-null object\n", - "OriginCityName 13059 non-null object\n", - "OriginCountry 13059 non-null object\n", - "OriginLocation 13059 non-null object\n", - "OriginRegion 13059 non-null object\n", - "OriginWeather 13059 non-null object\n", - "dayOfWeek 13059 non-null int64\n", - "timestamp 13059 non-null datetime64[ns]\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 AvgTicketPrice 13059 non-null float64 \n", + " 1 Cancelled 13059 non-null bool \n", + " 2 Carrier 13059 non-null object \n", + " 3 Dest 13059 non-null object \n", + " 4 DestAirportID 13059 non-null object \n", + " 5 DestCityName 13059 non-null object \n", + " 6 DestCountry 13059 non-null object \n", + " 7 DestLocation 13059 non-null object \n", + " 8 DestRegion 13059 non-null object \n", + " 9 DestWeather 13059 non-null object \n", + " 10 DistanceKilometers 13059 non-null float64 \n", + " 11 DistanceMiles 13059 non-null float64 \n", + " 12 FlightDelay 13059 non-null bool \n", + " 13 FlightDelayMin 13059 non-null int64 \n", + " 14 FlightDelayType 13059 non-null object \n", + " 15 FlightNum 13059 non-null object \n", + " 16 FlightTimeHour 13059 non-null float64 \n", + " 17 FlightTimeMin 13059 non-null float64 \n", + " 18 Origin 13059 non-null object \n", + " 19 OriginAirportID 13059 non-null object \n", + " 20 OriginCityName 13059 non-null object \n", + " 21 OriginCountry 13059 non-null object \n", + " 22 OriginLocation 13059 non-null object \n", + " 23 OriginRegion 13059 non-null object \n", + " 24 OriginWeather 13059 non-null object \n", + " 25 dayOfWeek 13059 non-null int64 \n", + " 26 timestamp 13059 non-null datetime64[ns]\n", "dtypes: bool(2), datetime64[ns](1), float64(5), int64(2), object(17)\n", - "memory usage: 96.0 bytes\n" + "memory usage: 80.0 bytes\n" ] } ], @@ -2900,16 +2904,16 @@ { "data": { "text/plain": [ - "AvgTicketPrice 1199.729053\n", - "Cancelled 1.000000\n", - "DistanceKilometers 19881.482315\n", - "DistanceMiles 12353.780369\n", - "FlightDelay 1.000000\n", - "FlightDelayMin 360.000000\n", - "FlightTimeHour 31.715034\n", - "FlightTimeMin 1902.902032\n", - "dayOfWeek 6.000000\n", - "dtype: float64" + "AvgTicketPrice 1199.73\n", + "Cancelled True\n", + "DistanceKilometers 19881.5\n", + "DistanceMiles 12353.8\n", + "FlightDelay True\n", + "FlightDelayMin 360\n", + "FlightTimeHour 31.715\n", + "FlightTimeMin 1902.9\n", + "dayOfWeek 6\n", + "dtype: object" ] }, "execution_count": 43, @@ -2940,16 +2944,16 @@ { "data": { "text/plain": [ - "AvgTicketPrice 1199.729004\n", - "Cancelled 1.000000\n", - "DistanceKilometers 19881.482422\n", - "DistanceMiles 12353.780273\n", - "FlightDelay 1.000000\n", - "FlightDelayMin 360.000000\n", - "FlightTimeHour 31.715034\n", - "FlightTimeMin 1902.901978\n", - "dayOfWeek 6.000000\n", - "dtype: float64" + "AvgTicketPrice 1199.73\n", + "Cancelled True\n", + "DistanceKilometers 19881.5\n", + "DistanceMiles 12353.8\n", + "FlightDelay True\n", + "FlightDelayMin 360\n", + "FlightTimeHour 31.715\n", + "FlightTimeMin 1902.9\n", + "dayOfWeek 6\n", + "dtype: object" ] }, "execution_count": 44, @@ -2980,16 +2984,16 @@ { "data": { "text/plain": [ - "AvgTicketPrice 100.020528\n", - "Cancelled 0.000000\n", - "DistanceKilometers 0.000000\n", - "DistanceMiles 0.000000\n", - "FlightDelay 0.000000\n", - "FlightDelayMin 0.000000\n", - "FlightTimeHour 0.000000\n", - "FlightTimeMin 0.000000\n", - "dayOfWeek 0.000000\n", - "dtype: float64" + "AvgTicketPrice 100.021\n", + "Cancelled False\n", + "DistanceKilometers 0\n", + "DistanceMiles 0\n", + "FlightDelay False\n", + "FlightDelayMin 0\n", + "FlightTimeHour 0\n", + "FlightTimeMin 0\n", + "dayOfWeek 0\n", + "dtype: object" ] }, "execution_count": 45, @@ -3013,16 +3017,16 @@ { "data": { "text/plain": [ - "AvgTicketPrice 100.020531\n", - "Cancelled 0.000000\n", - "DistanceKilometers 0.000000\n", - "DistanceMiles 0.000000\n", - "FlightDelay 0.000000\n", - "FlightDelayMin 0.000000\n", - "FlightTimeHour 0.000000\n", - "FlightTimeMin 0.000000\n", - "dayOfWeek 0.000000\n", - "dtype: float64" + "AvgTicketPrice 100.021\n", + "Cancelled False\n", + "DistanceKilometers 0\n", + "DistanceMiles 0\n", + "FlightDelay False\n", + "FlightDelayMin 0\n", + "FlightTimeHour 0\n", + "FlightTimeMin 0\n", + "dayOfWeek 0\n", + "dtype: object" ] }, "execution_count": 46, @@ -3590,9 +3594,27 @@ } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/daniel/PycharmProjects/eland/venv/lib/python3.6/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: \n", + "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n", + " layout[ax.rowNum, ax.colNum] = ax.get_visible()\n", + "/home/daniel/PycharmProjects/eland/venv/lib/python3.6/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: \n", + "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n", + " layout[ax.rowNum, ax.colNum] = ax.get_visible()\n", + "/home/daniel/PycharmProjects/eland/venv/lib/python3.6/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: \n", + "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n", + " if not layout[ax.rowNum + 1, ax.colNum]:\n", + "/home/daniel/PycharmProjects/eland/venv/lib/python3.6/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: \n", + "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n", + " if not layout[ax.rowNum + 1, ax.colNum]:\n" + ] + }, { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -3619,7 +3641,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -3659,7 +3681,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 59, "metadata": { "pycharm": { "is_executing": false @@ -3713,7 +3735,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.6.9" }, "pycharm": { "stem_cell": { diff --git a/eland/dataframe.py b/eland/dataframe.py index 96934ec1..23a65c6c 100644 --- a/eland/dataframe.py +++ b/eland/dataframe.py @@ -22,7 +22,7 @@ from pandas.core.computation.eval import eval from pandas.core.dtypes.common import is_list_like from pandas.core.indexing import check_bool_indexer -from pandas.io.common import _expand_user, _stringify_path +from pandas.io.common import _expand_user, stringify_path from pandas.io.formats import console from pandas.io.formats import format as fmt from pandas.io.formats.printing import pprint_thing @@ -249,12 +249,19 @@ def tail(self, n=5): -------- >>> df = ed.DataFrame('localhost', 'flights', columns=['Origin', 'Dest']) >>> df.tail() - Origin Dest - 13054 Pisa International Airport Xi'an Xianyang International Airport - 13055 Winnipeg / James Armstrong Richardson Internat... Zurich Airport - 13056 Licenciado Benito Juarez International Airport Ukrainka Air Base - 13057 Itami Airport Ministro Pistarini International Airport - 13058 Adelaide International Airport Washington Dulles International Airport + Origin \\ + 13054 Pisa International Airport + 13055 Winnipeg / James Armstrong Richardson International Airport + 13056 Licenciado Benito Juarez International Airport + 13057 Itami Airport + 13058 Adelaide International Airport + + Dest + 13054 Xi'an Xianyang International Airport + 13055 Zurich Airport + 13056 Ukrainka Air Base + 13057 Ministro Pistarini International Airport + 13058 Washington Dulles International Airport [5 rows x 2 columns] """ @@ -602,8 +609,10 @@ def info( Index: 4675 entries, 0 to 4674 Data columns (total 2 columns): - customer_first_name 4675 non-null object - geoip.city_name 4094 non-null object + # Column Non-Null Count Dtype + --- ------ -------------- ----- + 0 customer_first_name 4675 non-null object + 1 geoip.city_name 4094 non-null object dtypes: object(2) memory usage: ... """ @@ -618,6 +627,7 @@ def info( return cols = self.columns + col_count = len(self.columns) # hack if max_cols is None: @@ -637,30 +647,74 @@ def _put_str(s, space): def _verbose_repr(): lines.append(f"Data columns (total {len(self.columns)} columns):") - space = max(len(pprint_thing(k)) for k in self.columns) + 4 + + id_head = " # " + column_head = "Column" + col_space = 2 + + max_col = max(len(pprint_thing(k)) for k in cols) + len_column = len(pprint_thing(column_head)) + space = max(max_col, len_column) + col_space + + max_id = len(pprint_thing(col_count)) + len_id = len(pprint_thing(id_head)) + space_num = max(max_id, len_id) + col_space counts = None - tmpl = "{count}{dtype}" + header = _put_str(id_head, space_num) + _put_str(column_head, space) if show_counts: counts = self.count() if len(cols) != len(counts): # pragma: no cover raise AssertionError( - f"Columns must equal counts " - f"({len(cols):d} != {len(counts):d})" + "Columns must equal counts " + "({cols:d} != {counts:d})".format( + cols=len(cols), counts=len(counts) + ) ) - tmpl = "{count} non-null {dtype}" + count_header = "Non-Null Count" + len_count = len(count_header) + non_null = " non-null" + max_count = max(len(pprint_thing(k)) for k in counts) + len(non_null) + space_count = max(len_count, max_count) + col_space + count_temp = "{count}" + non_null + else: + count_header = "" + space_count = len(count_header) + len_count = space_count + count_temp = "{count}" + + dtype_header = "Dtype" + len_dtype = len(dtype_header) + max_dtypes = max(len(pprint_thing(k)) for k in self.dtypes) + space_dtype = max(len_dtype, max_dtypes) + header += _put_str(count_header, space_count) + _put_str( + dtype_header, space_dtype + ) + + lines.append(header) + lines.append( + _put_str("-" * len_id, space_num) + + _put_str("-" * len_column, space) + + _put_str("-" * len_count, space_count) + + _put_str("-" * len_dtype, space_dtype) + ) dtypes = self.dtypes for i, col in enumerate(self.columns): dtype = dtypes.iloc[i] col = pprint_thing(col) + line_no = _put_str(" {num}".format(num=i), space_num) + count = "" if show_counts: count = counts.iloc[i] lines.append( - _put_str(col, space) + tmpl.format(count=count, dtype=dtype) + line_no + + _put_str(col, space) + + _put_str(count_temp.format(count=count), space_count) + + _put_str(dtype, space_dtype) ) def _non_verbose_repr(): @@ -769,7 +823,7 @@ def to_html( df = self._build_repr(max_rows + 1) if buf is not None: - _buf = _expand_user(_stringify_path(buf)) + _buf = _expand_user(stringify_path(buf)) else: _buf = StringIO() @@ -866,7 +920,7 @@ def to_string( df = self._build_repr(max_rows + 1) if buf is not None: - _buf = _expand_user(_stringify_path(buf)) + _buf = _expand_user(stringify_path(buf)) else: _buf = StringIO() diff --git a/eland/ndframe.py b/eland/ndframe.py index dadc8e8a..d1b995a1 100644 --- a/eland/ndframe.py +++ b/eland/ndframe.py @@ -238,16 +238,16 @@ def min(self, numeric_only=True): -------- >>> df = ed.DataFrame('localhost', 'flights') >>> df.min() - AvgTicketPrice 100.020531 - Cancelled 0.000000 - DistanceKilometers 0.000000 - DistanceMiles 0.000000 - FlightDelay 0.000000 - FlightDelayMin 0.000000 - FlightTimeHour 0.000000 - FlightTimeMin 0.000000 - dayOfWeek 0.000000 - dtype: float64 + AvgTicketPrice 100.021 + Cancelled False + DistanceKilometers 0 + DistanceMiles 0 + FlightDelay False + FlightDelayMin 0 + FlightTimeHour 0 + FlightTimeMin 0 + dayOfWeek 0 + dtype: object """ return self._query_compiler.min(numeric_only=numeric_only) @@ -270,16 +270,16 @@ def max(self, numeric_only=True): -------- >>> df = ed.DataFrame('localhost', 'flights') >>> df.max() - AvgTicketPrice 1199.729004 - Cancelled 1.000000 - DistanceKilometers 19881.482422 - DistanceMiles 12353.780273 - FlightDelay 1.000000 - FlightDelayMin 360.000000 - FlightTimeHour 31.715034 - FlightTimeMin 1902.901978 - dayOfWeek 6.000000 - dtype: float64 + AvgTicketPrice 1199.73 + Cancelled True + DistanceKilometers 19881.5 + DistanceMiles 12353.8 + FlightDelay True + FlightDelayMin 360 + FlightTimeHour 31.715 + FlightTimeMin 1902.9 + dayOfWeek 6 + dtype: object """ return self._query_compiler.max(numeric_only=numeric_only) diff --git a/eland/operations.py b/eland/operations.py index 578ecfcf..adc89ee9 100644 --- a/eland/operations.py +++ b/eland/operations.py @@ -126,10 +126,14 @@ def sum(self, query_compiler, numeric_only=True): return self._metric_aggs(query_compiler, "sum", numeric_only=numeric_only) def max(self, query_compiler, numeric_only=True): - return self._metric_aggs(query_compiler, "max", numeric_only=numeric_only) + return self._metric_aggs( + query_compiler, "max", numeric_only=numeric_only, keep_original_dtype=True + ) def min(self, query_compiler, numeric_only=True): - return self._metric_aggs(query_compiler, "min", numeric_only=numeric_only) + return self._metric_aggs( + query_compiler, "min", numeric_only=numeric_only, keep_original_dtype=True + ) def nunique(self, query_compiler): return self._metric_aggs( @@ -142,13 +146,22 @@ def value_counts(self, query_compiler, es_size): def hist(self, query_compiler, bins): return self._hist_aggs(query_compiler, bins) - def _metric_aggs(self, query_compiler, func, field_types=None, numeric_only=None): + def _metric_aggs( + self, + query_compiler, + func, + field_types=None, + numeric_only=None, + keep_original_dtype=False, + ): """ Parameters ---------- field_types: str, default None if `aggregatable` use only field_names whose fields in elasticseach are aggregatable. If `None`, use only numeric fields. + keep_original_dtype : bool, default False + if `True` the output values should keep the same domain as the input values, i.e. booleans should be booleans Returns ------- @@ -235,6 +248,10 @@ def _metric_aggs(self, query_compiler, func, field_types=None, numeric_only=None results[field] = elasticsearch_date_to_pandas_date( response["aggregations"][field]["value_as_string"], date_format ) + elif keep_original_dtype: + results[field] = pd_dtype.type( + response["aggregations"][field]["value"] + ) else: results[field] = response["aggregations"][field]["value"] diff --git a/eland/series.py b/eland/series.py index 241d7226..9b597a60 100644 --- a/eland/series.py +++ b/eland/series.py @@ -35,7 +35,7 @@ import numpy as np import pandas as pd -from pandas.io.common import _expand_user, _stringify_path +from pandas.io.common import _expand_user, stringify_path import eland.plotting from eland import NDFrame @@ -365,7 +365,7 @@ def to_string( temp_series = self._build_repr(max_rows + 1) if buf is not None: - _buf = _expand_user(_stringify_path(buf)) + _buf = _expand_user(stringify_path(buf)) else: _buf = StringIO() diff --git a/noxfile.py b/noxfile.py index aec7c992..7a20451c 100644 --- a/noxfile.py +++ b/noxfile.py @@ -20,7 +20,7 @@ def blacken(session): def lint(session): session.install("black", "flake8") session.run("black", "--check", "--target-version=py36", *SOURCE_FILES) - session.run("flake8", "--ignore=E501,W503,E402,E712", *SOURCE_FILES) + session.run("flake8", "--ignore=W291,E501,W503,E402,E712", *SOURCE_FILES) @nox.session(python=["3.6", "3.7", "3.8"]) diff --git a/requirements-dev.txt b/requirements-dev.txt index d11a0b42..6d15fb8c 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,5 +1,5 @@ elasticsearch>=7.0.5 -pandas==0.25.3 +pandas>=1 matplotlib pytest>=5.2.1 nbval diff --git a/requirements.txt b/requirements.txt index b291345b..5a356647 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,3 @@ elasticsearch>=7.0.5 -pandas==0.25.3 +pandas>=1 matplotlib diff --git a/setup.py b/setup.py index a31dbfb4..04ba1ec3 100644 --- a/setup.py +++ b/setup.py @@ -187,6 +187,6 @@ classifiers=CLASSIFIERS, keywords="elastic eland pandas python", packages=find_packages(include=["eland", "eland.*"]), - install_requires=["elasticsearch>=7.0.5, <8", "pandas==0.25.3", "matplotlib"], + install_requires=["elasticsearch>=7.0.5, <8", "pandas>=1", "matplotlib"], python_requires=">=3.6", )